When the LiDAR points are sparse, there are multiple configurations of bounding boxes that could explain the observed data.
By predicting a multimodal distribution at each point, we can further improve the recall of our method.
It is inappropriate to use NMS with a strict threshold when producing a multimodal distribution because only the most likely bounding box will persist.
Alternatively, we could use Soft NMS [1]} to re-weight the confidence instead of eliminating boxes, but this breaks our probabilistic interpretation of the confidence.
By leveraging our adaptive NMS algorithm, we maintain our probabilistic interpretation and obtain better performance.
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